{"id":"W4235527823","doi":"10.32920/ryerson.14656314","title":"Architecture without barriers: designing inclusive environments accessible to all","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Urban Design and Spatial Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sheridan College; Toronto Metropolitan University","funders":"","keywords":"Architecture; Dignity; Universal design; Equity (law); Social equality; Demographics; Population; Intervention (counseling); Psychology; Computer science; Political science; Sociology; Geography; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001265448,0.0004542991,0.000558629,0.000253875,0.0000744609,0.0002438276,0.0005319073,0.0003275552,0.001572819],"category_scores_gemma":[0.00004111708,0.0004318297,0.0002220667,0.0001837037,0.00001969812,0.00007289642,0.001022033,0.0006942527,0.0000833751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002275664,"about_ca_system_score_gemma":0.00006270268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002096578,"about_ca_topic_score_gemma":0.0002326787,"domain_scores_codex":[0.998251,0.00007172032,0.0003435618,0.000563642,0.0003698259,0.0004003172],"domain_scores_gemma":[0.9988644,0.00003399323,0.0000497787,0.0006177259,0.00001925724,0.0004148537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001094133,0.00001727448,0.001507227,0.0001416355,0.001039181,0.00006510392,0.004126942,0.9502447,0.03433417,0.000005136745,0.003512559,0.004995089],"study_design_scores_gemma":[0.001722318,0.0002138444,0.002749097,0.001659498,0.002752632,0.00003134906,0.003582116,0.5126712,0.3975056,0.002702801,0.06620571,0.008203824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05399961,0.0005880416,0.9369783,0.0002237105,0.0003206067,0.0004069789,0.00001682849,0.0003312384,0.007134637],"genre_scores_gemma":[0.9444693,0.0001053193,0.05097745,0.0008797101,0.0002151331,0.00009709517,0.0001208483,0.0001088562,0.003026325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8904697,"threshold_uncertainty_score":0.9998134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01211197022532814,"score_gpt":0.2349009865545282,"score_spread":0.2227890163292001,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}